Nutonian, a Boston-based big data analytics startup with roots in scientific and academic circles, said Wednesday that it has landed a $4 million Series A funding round led by Atlas Venture.

Nutonian will use the funding to further development of its flagship software, Eureqa, which uses a machine learning technique called symbolic regression to find and explain correlations and trends within large amounts of data.

Eureqa goes beyond traditional machine learning research by not only finding unseen relationships in data but also explaining why a particular prediction is being made, Michael Schmidt, founder and CEO of Nutonian, said in an interview.

"There is no one that gives you the prediction and the reason why, and that's what we do," Schmidt said. "Eureqa is for anyone who wants to predict something, optimize something or understand something."

Schmidt began working on the technology behind Eureqa while at graduate school at Cornell. He told CRN he originally designed it to help scientists tackle huge engineering projects with lots of data, while also giving them insight into why specific things are happening.

Eureqa already has traction in science and academic circles. Astronomers are using it to figure out how galaxies form, and medical researchers are using it to detect early signs of macular degeneration, a vision problem linked to retina damage, Schmidt said.

Nutonian, which already has 40,000 Eureqa users and 3,000 paying customers, is now casting a wider net for enterprises that find trends within their data and want to understand what's driving them. In the past six months, Nutonian has also hired engineers Andrew Lamb and Alison Reynolds, formerly of Hewlett-Packard's Vertica unit.

"Business problems are the same types of problems that scientists have when they want to understand more complex systems," Schmidt said.

Chris Lynch, a partner with Atlas Venture and former CEO of Vertica, described Nutonian's technology as "artificial intelligence meets business intelligence."

Eureqa "ingests unstructured and semi-structured data and lets you ask queries against it," Lynch said in an interview. After analyzing the data, Eureqa develops a series of questions, using machine learning, for uses to ask in order to test the insights it has identified, Lynch said in an interview.

Lynch said this technology could help an airline manufacturer better understand the life cycle for airline components, which is the sort of insight that could save millions of dollars.

"Eureqa takes in the material composition of the component, the physics data, and data about weight distribution and flight times, and predicts when a component will fail," Lynch said. Armed with this information, an airline could replace only the parts that are susceptible to failure, he said.

Retailers could use Eureqa to analyze point-of-sale data, weather data, demographic data and the characteristics of the products they're selling, and get back recommended inventory levels and suggestions on where to place items within a store, Lynch said.

Nutonian currently sells desktop and server versions of Eureqa, and it's working on a suite of products, called Eureqa Enterprise, which is currently in invitation-only mode.

Nutonian "has a lot more coming down the road" in terms of products and services aimed at a more general audience, Schmidt said.